Deriving image data from data received from an ultrasound probe

1. Apparatus for producing image data representing a specimen being imaged using ultrasound, comprising a processor and memory, wherein said processor is configured to: receive input data representing an output from an ultrasound probe; obtain, from said memory, a first matrix that is dependent upon attributes of said probe and upon the way in which said input data was produced, and a second matrix that is the inverse of the sum of a positive scalar matrix having the same dimensions as said first matrix, and the product of a Hermitian transpose of the first matrix, and the first matrix; initialize an estimate of the mean of the distribution of said image data using said first matrix and said input data; initialize estimates of echogenicity of said image data and variance of noise of said image data; iteratively perform the following steps until a predetermined level of convergence is reached: (a) update said estimate of the mean of the distribution of said image data using: said first matrix, a respective said estimate of said echogenicity, a respective said estimate of said variance of said noise, and said input data, and (b) update said estimate of the echogenicity and said estimate of the variance of the noise using said updated estimate of the mean of the distribution of said image data, wherein said processor is configured to perform at least one iteration of step (a) using an algorithm that is preconditioned with second matrix; and using a converged estimate of the mean of the distribution of said image data to produce output image data for display.

2. Apparatus according to claim 1 , wherein said processor is configured to additionally use said second matrix when initializing the estimate of the mean of the distribution of said image data.

3. Apparatus according to claim 2 , wherein said processor is configured to initialize said estimate of the mean of the distribution of said image data by calculating the product of: (a) said second matrix, (b) a Hermitian transpose of said first matrix, and (c) said image data.

4. Apparatus according to claim 1 , wherein said processor is further configured to evaluate said second matrix by: transforming said first matrix using a Fourier transform; splitting said transformed first matrix into a plurality of first submatrices; for each said first submatrix, calculating a second submatrix using said first submatrix and said constant according to the calculation given using said second matrix when initializing the estimate of the mean of the distribution of said image data; combining said second submatrices to create a transformed second matrix; and transforming said transformed second matrix using the inverse of said Fourier transform to create said second matrix.

5. Apparatus according to claim 1 , wherein said constant represents a noise-to-signal ratio of the image data.

6. Apparatus according to claim 1 , wherein said algorithm used in step (a) is the Conjugate Gradients algorithm.

7. Apparatus according to claim 1 , wherein said processor is configured to additionally calculate a covariance of the distribution of said image data and use it to produce said output image data.

8. Apparatus according to claim 1 , wherein said processor is configured to update the estimate of the echogenicity additionally using said input data.

9. Apparatus according to claim 1 , wherein said processor is configured to update the estimate of the variance of the noise of the image data additionally using said first matrix and said input data.

10. A method of producing image data representing a specimen being imaged using ultrasound, comprising the steps of: receiving input data representing an output from an ultrasound probe; obtaining a first matrix that is dependent upon attributes of said probe and upon the way in which said input data was produced; obtaining a second matrix that is the inverse of the sum of a positive scalar matrix having the same dimensions as said first matrix, and the product of a Hermitian transpose of the first matrix, and the first matrix; initializing an estimate of the mean of the distribution of said image data using said first matrix and said input data; initializing estimates of echogenicity of said image data and variance of noise of said image data; iteratively performing the following steps until a predetermined level of convergence is reached: (a) update said estimate of the mean of the distribution of said image data using: said first matrix, a respective said estimate of said echogenicity, a respective said estimate of said variance of said noise, and said input data, and (b) update said estimate of the echogenicity and said estimate of the variance of the noise using said updated estimate of the mean of the distribution of said image data, wherein at least one iteration of step (a) is performed using an algorithm that is preconditioned with said second matrix; and using a converged estimate of the mean of the distribution of said image data to produce output image data for display.

11. A method according to claim 10 , wherein said step of initializing the estimate of the mean of the distribution of said image data is carried out additionally using said second matrix.

12. A method according to claim 11 , wherein said step of initializing the estimate of the mean of the distribution of said image data comprises calculating the product of: (a) said second matrix, (b) a Hermitian transpose of said first matrix, and (c) said image data.

13. A method according to claim 10 , wherein said step of evaluating said second matrix comprises: transforming said first matrix using a Fourier transform; splitting said transformed first matrix into a plurality of first submatrices; for each said first submatrix, calculating a second submatrix using said first submatrix and said constant according to the calculation given using said second matrix when initializing the estimate of the mean of the distribution of said image data; combining said second submatrices to create a transformed second matrix; and transforming said transformed second matrix using the inverse of said Fourier transform to create said second matrix.

14. A method according to claim 10 , wherein said constant represents a noise-to-signal ratio of the image data.

15. A method according to claim 10 , wherein said algorithm used in step (a) is the Conjugate Gradients algorithm.

16. A method according to claim 10 , further including the step of calculating a covariance of the distribution of said image data and using it to produce said output image data.

17. A method according to claim 10 , wherein said step of updating the estimate of the variance of the noise of the image data is carried out additionally using said first matrix and said input data.

18. A non-transitory computer-readable medium encoded with: program instructions executable by a computer for processing input data representing an output from an ultrasound probe; a first matrix that is dependent upon attributes of said probe and upon the way in which said input data was produced; and a second matrix, that is the inverse of the sum of a positive scalar matrix having the same dimensions as said first matrix, and the product of a Hermitian transpose of the first matrix, and the first matrix; and wherein said program instructions, when executed by a the computer, cause the computer to perform steps of: receiving said input data from said ultrasound probe; obtaining said first matrix and said second matrix; initializing an estimate of the mean of the distribution of said image data using said first matrix and said input data; initializing estimates of the echogenicity of said image data and variance of noise of said image data; iteratively performing the following steps until a predetermined level of convergence is reached: (a) update said estimate of the mean of the distribution of said image data using: said first matrix, a respective said estimate of said echogenicity, a respective said estimate of said variance of said noise, and said input data, and (b) update said estimate of the echogenicity and said estimate of the variance of the noise using said updated estimate of the mean of the distribution of said image data, wherein each iteration of step (a) is performed using an algorithm that is preconditioned with said second matrix; and using said converged estimate of the mean of the distribution of said image data to produce output image data for a display.

19. A method of performing deconvolution on data received from an ultrasound probe, comprising the steps of: obtaining a blurring matrix (H) that encapsulates the ultrasound blur function associated with said ultrasound probe; obtaining a preconditioning matrix (P −1 ) that is the sum of: the product of a Hermitian transpose of said blurring matrix, and the blurring matrix; and a positive scalar matrix that is the product of a positive constant (η) and an identity matrix of the same dimension as the blurring matrix; i.e., P −1 =(H H H+η I H ) −1 ; and evaluating an estimate of the mean of the distribution of the data received from the ultrasound probe by using an Expectation Maximization algorithm, in which at least one Expectation step thereof is performed using a Conjugate Gradients algorithm that is preconditioned using said inverse of the preconditioning matrix.